FAT4 Overexpression in Peripheral Blood Mononuclear Cells and Tumor Tissues is Associated With a Favorable Prognosis and Promotes Immune Cell Infiltration in Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-1648031/v1

Download PDF

Research Article

FAT4 Overexpression in Peripheral Blood Mononuclear Cells and Tumor Tissues is Associated With a Favorable Prognosis and Promotes Immune Cell Infiltration in Hepatocellular Carcinoma

https://doi.org/10.21203/rs.3.rs-1648031/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Purpose: To identify prognostically relevant genes in hepatocellular carcinoma, and to investigate whether gene expression in peripheral blood mononuclear cells (PBMCs) and hepatocellular carcinoma tissues can be used as a diagnostic and prognostic marker.

Methods: Using RNA-sequencing, gene expression was examined in PBMCs from patients with advanced hepatocellular carcinoma who survived or died. The expression of the top 10 genes in the survival group was validated.

Results: The Gene Expression Omnibus and The Cancer Genome Atlas databases were used to analyze FAT atypical cadherin 4 (FAT4) transcript levels in PBMCs and in hepatocellular carcinoma and normal tissues. FAT4 expression was lower in hepatocellular carcinoma than in normal PBMCs and tissues. Kaplan–Meier analysis suggested that downregulated FAT4 was associated with a relatively poor prognosis. The correlation between FAT4 expression and immune cell infiltration was also explored using Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis 2, and the Tumor and Immune System Interaction Database. FAT4 overexpression was positively correlated with immune cell infiltration, several immune cell markers, and immune checkpoint expression. MicroRNAs regulating FAT4 expression were identified using correlation, expression, and survival analyses. Hsa-miR-93-5p was the most likely upstream microRNA of FAT4 in hepatocellular carcinoma.

Conclusion: FAT4 is highly expressed in PBMCs and hepatocellular carcinoma tissues, indicating a favorable prognosis and immune cell infiltration in hepatocellular carcinoma, and revealing the potential role of miRNA-93-5p-mediated downregulation of FAT4 in hepatocellular carcinoma immunology. Our findings provide a new direction for the development of novel immunotherapy targets for hepatocellular carcinoma.

Biomarker

FAT Atypical Cadherin 4

Hepatocellular Carcinoma

Immune Cell Infiltration

MicroRNA

Peripheral Blood Mononuclear Cell

Primary liver cancer is a global health problem. Approximately 90% of primary liver cancer cases are hepatocellular carcinoma (HCC), which is the second most lethal cancer, with a 5-year survival rate of only 18% (Yang et al. 2019). Approximately 40% of HCC cases worldwide originate from China (Wang and Li 2019; Yang et al. 2019). HCC is usually caused by chronic liver diseases, such as HBV- and HCV-related infections; alcoholic, metabolic, and dietary fatty liver diseases; and autoimmune or chronic cholesterol diseases (Wallace et al. 2015). Surgery may be effective in the early stages of HCC. However, most patients with HCC have entered the advanced stage of the disease at the time of diagnosis, and thus missed the opportunity for surgery (Qureshi et al. 2021). Rapid tumor progression and metastasis are the main reasons for the short survival time and high mortality of patients with advanced HCC (Jiang et al. 2019). For patients who miss the opportunity for surgery, the application of molecular-targeted therapy and immune checkpoint inhibitors has become more important (Qureshi et al. 2021). Several studies have shown that tumor-infiltrating immune cells, such as tumor-associated macrophages (Yan and Wan 2021), affect the prognosis and efficacy of chemotherapy and immunotherapy, as well as tumor angiogenesis, progression, and metastasis (Sperandio et al. 2022; Zhang and Chen 2020). Therefore, it is necessary to explore a reliable immune-related biomarker to evaluate prognosis and immune infiltration in advanced HCC.

Peripheral blood mononuclear cells (PBMCs) are immune cells (such as monocytes and lymphocytes) that act as the first line of defense for the immune system against cancer (Li et al. 2019b). Cancer causes various phenotypic and functional changes in immune cells during the interaction between cancer and immune cells. Differential expression of PBMC genes may be related to cancer immunogenicity, or may occur after immune evasion (Salman et al. 2012; Chang et al. 2013). A study found that expanded T cell receptor (TCR) clones of newly infiltrating CD8 + T cells in a tumor were significantly correlated with expanded peripheral clones (Wu et al. 2020). Patients with a good response to programmed cell death protein 1 (PD-1)/Programmed cell death 1 ligand 1 (PD-L1) blockade had persistent tumor immune circulating activity in vivo, indicating that CD8 + T cells in tumor tissues in HCC can be replenished by peripheral CD8 + T cells. This in turn suggests that peripheral immune cells may reflect local immune cell invasion in the tumor microenvironment (Hogan et al. 2019; Pallet et al. 2020). Therefore, the differential expression of genes in PBMCs may potentially reflect the immune status of the host, and the analysis of peripheral immune cells may be suitable for evaluating the prognosis of patients with HCC, and the host immune response to cancer (Wu et al. 2020). If we can evaluate the differential expression of PBMC genes to explore the immune response to cancer progression or the efficacy of immune-related treatment, this will be clinically useful because blood sample collection is more patient-friendly than tumor tissue sampling.

In this study, we investigated whether the expression of prognostically relevant genes in PBMCs and HCC tissues can be used as a diagnostic and prognostic marker. We successfully identified FAT atypical cadherin 4 (FAT4) as a prognostically relevant gene in PBMCs and HCC tissues. We then evaluated the relationship between FAT4 expression, tumor-infiltrating immune cells, and immune cell markers. Moreover, we explored the mechanisms by which microRNAs (miRNAs) regulate FAT4 in HCC. Our findings provide insights into the role of miRNA-mediated downregulation of FAT4 in HCC prognosis and tumor immune infiltration.

Patient Samples

HCC is staged as follows, according to the Barcelona Clinic Liver Cancer (BCLC) staging system: stage 0 (very early), stage A (early), stage B (intermediate), stage C (advanced), and stage D (terminal). Different treatments are indicated for each stage (Ayuso et al. 2018; Llovet et al. 1999; Parikh and Pillai 2021). Between January 2018 and December 2019, 12 patients with BCLC stage C or D HCC were recruited in this study. The demographic characteristics are provided in Online Resource 1. We collected 8 mL of peripheral blood from each patient. RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions, and was subsequently sequenced. Patients were followed-up regularly. Between January 2020 and January 2021, 15 patients were recruited into the validation cohort: healthy (n = 3), stage A (n = 3), stage B (n = 3), stage C (n = 3), and stage D (n = 3). The demographic characteristics of the validation cohort are presented in Online Resource 2. Blood sampling, RNA extraction, and RNA-sequencing (RNA-seq) were performed using the same method as that used for the HCC cohort.

RNA-Seq Library

Illumina TruSeq RNA sample preparation guidelines were followed (Illumina, San Diego, CA, USA). Ribosomal RNA was removed from the RNA samples. Messenger RNA (mRNA) was enriched and then reverse-transcribed into complementary deoxyribonucleic acid (DNA). Sequencing connectors were added to the double-stranded DNA. Libraries were quality-tested and quantified using a 2100 Bioanalyzer and RIN Beta Version Software (Agilent, Santa Clara, California). Paired-end sequencing (2 × 150 bp) was performed using an Illumina HiSeq X TEN Reagent Kit (NEB, USA).

Differentially Expressed Genes (DEGs)

Raw RNA-seq data were filtered using SOAPnuke (version 1.0.1). High-quality clean reads were aligned with the human transcriptome and HG19 genome using TopHat2 (version 2.0.7). All genes with a sample size of 0 were eliminated. The R packages DESeq2, edgeR, and limma were used to screen for DEGs. A Venn diagram was constructed to compare DEGs in each R package by Venny 2.1.

Gene Expression Analysis

FAT4 expression data and clinical information from datasets GSE60502, GSE84402, GSE101685, GSE36076, GSE49515, and GSE58208 were downloaded from the Gene Expression Omnibus (GEO) database and validated for FAT4 expression analysis. Raw data were downloaded as MINiML files. Box plots were drawn using boxplot; the ggord package was used to draw the Principal Component Analysis (PCA) plot. The UALCAN (http://ualcan.path.uab.edu/) database was used to investigate FAT4 expression in HCC, and to elucidate the relationship between FAT4 expression and various clinicopathological features of HCC (stage, race, sex, age, weight, grade, nodal metastasis, Tumor Protein P53 (TP53) mutation status, and tumor histology) (Chandrashekar et al. 2017). Additionally, we used the Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/) database to analyze the mRNA levels of FAT4 in several cancers (Li et al. 2020).

Survival Analysis

FAT4 expression levels affecting the clinical outcomes of patients with HCC were estimated using Kaplan–Meier survival curves (http://kmplot.com/analysis/) (Lánczky and Győrffy 2021). The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) (http://gepia.cancer-pku.cn/index.html) database was used to assess the effect of FAT4-related genes (mRNA, miRNA, and protein) on survival in 12 cancer types, and the potential prognostic value of FAT4 in 12 cancer types (Tang et al. 2017).

Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction Network

We utilized the LinkedOmics (http://www.linkedomics.org/login.php) database to identify co-expressed genes of FAT4 in HCC (Vasaikar et al. 2018). We used GSEA software to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of FAT4 signaling pathways in HCC and Gene Ontology (GO) analysis of FAT4 in HCC. Furthermore, we constructed a protein-protein interaction network of FAT4 using the STRING (https://string-db.org/) database.

Analysis of FAT4 Signaling Pathways in HCC

RNA expression (level 3) profiles and corresponding clinical information on HCC were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.com) database. Data were analyzed using the GSVA package in R. The selected parameter was method='ssgsea'. The correlation between the FAT4 gene and pathway scores was analyzed using Spearman’s correlation.

Prediction of Upstream miRNAs Regulating FAT4

StarBase was used to predict miRNAs that could potentially bind to FAT4 (Li et al. 2014). Upstream miRNAs of FAT4 were predicted using miRmap, microT, miRanda, PicTar, and TargetScan. In addition, we used starBase to perform expression correlation analysis between the predicted miRNAs and FAT4 in HCC.

Immune Infiltration Analysis

We used R Foundation for Statistical Computing (2020) version 4.0.3 and software packages ggplot2 and pheatmap to assess the immune score of various immune cells in HCC tissues with high or low FAT4 expression and in normal tissues. TIMER was used to examine the relationship between somatic copy number alterations in FAT4 and the infiltration levels of B cells, CD4+/CD8 + T cells, neutrophils, macrophages, and dendritic cells. We used the correlation modules in GEPIA2 and TIMER to analyze the relationship between FAT4 expression and markers of tumor-infiltrating immune cells in HCC using Spearman’s correlation coefficient (Li et al. 2020; Tang et al. 2017). In addition, we used the “immunostimulator” and “immunoinhibitor” modules in the Tumor and Immune System Interaction Database (TISIDB) (http://cis.hku.hk/TISIDB) to evaluate the relationship between FAT4 expression and immune checkpoints (Ru et al. 2019).

Statistical Analysis

All data analyses were performed using R (version 4.0.1) and SPSS software (version 22.0). PCA and Pearson’s correlation analysis were performed to evaluate the similarity of the samples, and Spearman’s correlation analysis was performed to analyze the correlation. Statistical differences were examined using t-test or one-way analysis of variance. All statistical tests were two-sided. Statistical significance was set at P < 0.05.

Identification of Prognostically Relevant Genes in PBMCs of Patients With HCC

The experimental roadmap used in this study is shown in Fig. 1. A total of 12 samples were analyzed, including six samples from BCLC stage C death cases, four samples from BCLC stage C survivors, and two samples from BCLC stage D survivors. We removed genes with a sample size of 0 from the RNA-seq data of PBMCs and screened 6,004 genes. With P < 0.05 and 95% log fold-change cutoff as screening criteria, the PBMC transcriptome confidence intervals of the two patient cohorts were compared using the DESeq2, edgeR, and limma R packages, showing 499, 894, and 427 DEGs, respectively (Fig. 2a). A Venn diagram revealed that 31 DEGs were common to the three datasets (Fig. 2b). DEGs were translated using the GENECODE v22 official annotation file. The heatmap is shown in Fig. 2c. The top 10 genes in survivors were DGKK, LIX1, NUAK1, CXCL10, FAT4, U62631.5, SLC1A7, COL13A1, IGLV3-12, and CTD-2532D12.4.

To further verify the expression of the top 10 genes in PBMCs from HCC survivors, we evaluated the quantitative expression of mRNA of DGKK, LIX1, NUAK1, CXCL10, FAT4, U62631.5, SLC1A7, COL13A1, IGLV3-12, and CTD-2532D12.4 in PBMCs of three patients with HCC with different BCLC stages by RNA-seq. The results showed that the mRNA expression of these 10 genes was downregulated in PBMCs of patients with stage D HCC (Fig. 2d–m). Except for FAT4, there were no significant differences between the expression levels of these genes, which may be due to the small number of samples. Furthermore, we observed that FAT4 mRNA expression in PBMCs correlated with BCLC stage; patients with more advanced cancer tended to express lower mRNA levels of FAT4 (Fig. 2m). Next, we used the GEO database (GSE36076, GSE49515, and GSE58208) to analyze FAT4 transcript levels in PBMCs from patients with HCC and healthy controls. The results showed that FAT4 mRNA expression in HCC PBMCs was lower than that in normal PBMCs (Fig. 2n, o).

Analysis of FAT4 Function in HCC

After determining that FAT4 is lowly expressed in HCC PBMCs, we explored the function of FAT4 in HCC progression. The LinkedOmics database was used to examine FAT4 expression in the TCGA-HCC cohort (Fig. 3a). Significant genes that were positively and negatively correlated with FAT4 are shown in the heatmap (Fig. 3b, c). KEGG pathway enrichment analysis showed that FAT4-co-expressed genes were mainly involved in focal adhesion, extracellular matrix (ECM)-receptor interaction, platelet activation, axon guidance, Rap1 signaling pathway, vascular smooth muscle contraction, AGE-RAGE signaling pathway in diabetes, arrhythmogenic right ventricular cardiomyopathy, Ras signaling pathway, and regulation of lipolysis in adipocytes (Fig. 3d).

For the GO term, GSEA further indicated that FAT4-co-expressed genes were mainly involved in the biological processes of vasculogenesis, angiogenesis, tissue migration, cell-substrate adhesion, respiratory system development, positive regulation of cell motility, regulation of vasculature development, respiratory tube development, neuron migration, and muscle cell migration (Fig. 3e). FAT4-co-expressed genes were mainly involved in the cellular components of the collagen trimer, extracellular matrix, cell-cell junction, basal part of the cell, receptor complex, membrane region, neuron spine, cell leading edge, platelet alpha granule, and protein complex involved in cell adhesion (Fig. 3f). FAT4-co-expressed genes were mainly involved in the molecular functions of the extracellular matrix structural constituent, GTPase binding, SMAD binding, transmembrane receptor protein kinase activity, guanyl-nucleotide exchange factor activity, collagen binding, growth factor binding, protein tyrosine kinase binding, phosphatidylinositol kinase activity, and cytokine binding (Fig. 3g). Finally, we used the STRING database to construct a protein-protein interaction network of FAT4 (Fig. 3h).

Analysis of FAT4-Related Signaling Pathways in HCC

To examine the mechanisms influenced by FAT4 in HCC, RNA expression (level 3) profiles and corresponding clinical information on HCC were downloaded from TCGA database. The correlation between the FAT4 gene and pathway scores was analyzed using Spearman’s correlation. We found that FAT4 expression was associated with several signaling pathways in HCC, including the PI3K/AKT/mTOR pathway, ECM degradation, transforming growth factor β, ECM-related genes, IL-10 anti-inflammatory signaling pathway, epithelial–mesenchymal transition (EMT) markers, collagen formation, apoptosis, p53 pathway, angiogenesis, and inflammatory response (Fig. 4a–k). Moreover, FAT4 expression was negatively correlated with the signaling pathways of DNA repair, MYC targets, G2M checkpoint, DNA replication, genes upregulated by reactive oxygen species, cellular response to hypoxia, and tumor proliferation signature (Fig. 4l–r).

FAT4 Expression in HCC and Prognosis

At the tissue level, the expression of FAT4 in HCC tumor and normal tissues was analyzed using the GEO (GSE60502, GSE84402, and GSE101685) and TCGA databases. The results showed that FAT4 mRNA expression was lower in HCC tissues than in normal tissues (Fig. 5a–d). We further revealed the association between FAT4 expression and various clinicopathological features (stage, race, sex, age, weight, grade, nodal metastasis, TP53 mutation status, and tumor histology) of HCC using the UALCAN database. The results showed that low FAT4 expression was significantly associated with stage, race, sex, age, weight, grade, TP53 mutation status, and tumor histology in patients with HCC (Online Resource 3).

Next, we explored the prognostic value of FAT4 mRNA expression in HCC using the Kaplan–Meier plotter database to estimate the influence of FAT4 expression on prognosis in patients with HCC. The results showed that increased FAT4 expression was associated with a good prognosis in patients with HCC (overall survival [OS]: hazard ratio [HR], 0.55 [95% confidence interval (CI): 0.39–0.78], log-rank P = 0.00069; relapse-free survival: HR, 0.5 [95% CI: 0.35–0.71], log-rank P = 8.3e-05; progression-free survival: HR, 0.51 [95% CI: 0.37–0.69], log-rank P = 1.5e-05; disease-specific survival: HR, 0.46 [95% CI: 0.30–0.72], log-rank P = 0.00051) (Fig. 5e–h). In the subgroup analysis, although the high FAT4 expression and OS (HR, 0.33 [95% CI: 0.10–1.17], log-rank P = 0.072) of HCC-Grade 1 patients did not reach statistical significance (Fig. 5i), the high FAT4 expression in HCC-Grade 2 patients was associated with a longer OS (HR, 0.48 [95% CI: 0.29–0.81], log-rank P = 0.0048) (Fig. 5j). Moreover, HCC-Grade 3 patients with high FAT4 expression had significantly longer OS (HR, 0.4 [95% CI: 0.20–0.78], log-rank P = 0.0053) (Fig. 5k). In addition, HCC-Stage 1 patients with high FAT4 expression also had longer OS (HR, 0.46 [95% CI: 0.25–0.84], log-rank P = 0.0096) (Fig. 5l). However, high FAT4 expression showed no OS benefit in HCC-Stage 2 patients (HR, 0.67 [95% CI: 0.30–1.48], log-rank P = 0.31) (Fig. 5m). Consistent with the findings observed in HCC-Stage 1 patients, high FAT4 expression was associated with a good prognosis in patients with HCC-Stage 1 + 2 (OS: HR, 0.52 [95% CI: 0.32–0.85], log-rank P = 0.0077) (Fig. 5n), HCC-Stage 3 (OS: HR, 0.46 [95% CI: 0.25–0.85], log-rank P = 0.012) (Fig. 5o), and HCC-Stage 3 + 4 (OS: HR, 0.46 [95% CI: 0.25–0.84], log-rank P = 0.0095) (Fig. 5p). Furthermore, we investigated the relationship between FAT4 expression and prognosis with distinct clinicopathological features in HCC. High FAT4 expression was associated with longer progression-free survival in men, women, American Joint Committee on Cancer T1 + T2, patients with vascular invasion, and patients with viral hepatitis (Online Resource 4). No significant differences in high FAT4 expression and OS were observed between Caucasian patients, male patients, and patients with vascular invasion (Online Resource 4).

FAT4 Downregulation is Correlated With MiR-93-5p Upregulation in HCC

To determine whether FAT4 is regulated by miRNAs, we screened eight potential target miRNAs using several target gene prediction programs, including miRmap, microT, miRanda, PicTar, and TargetScan (Online Resource 5). Based on the mechanism by which miRNAs recognize target mRNAs through complementary base pairing and guide silencing complexes to degrade target mRNAs or inhibit target mRNAs according to their degree of complementarity, it was speculated that the expression levels of miRNAs and FAT4 in HCC were negatively correlated. We assessed the correlation between predicted miRNAs and FAT4 expression levels in HCC using starBase. The results showed that hsa-miR-17-5p (r = -0.289, P = 1.58e-08), hsa-miR-20a-5p (r = -0.248, P = 1.37e-06), hsa-miR-93-5p (r = -0.271, P = 1.24e-07), and hsa-miR-193a-3p (r = -0.182, P = 4.35e-04) were inversely correlated with FAT4 expression (Fig. 6a). Subsequently, we further evaluated the expression levels of the above miRNAs in normal and HCC samples using TCGA (Fig. 6b–i). Combined with the above results, we found that the expression levels of hsa-miR-17-5p, hsa-miR-20a-5p, and hsa-miR-93-5p in HCC were significantly higher than those in normal samples, and were negatively correlated with the expression of FAT4, which supported our hypothesis. Next, we analyzed the prognostic value of hsa-miR-17-5p, hsa-miR-20a-5p, and hsa-miR-93-5p in HCC using Kaplan–Meier curves. The results indicated that patients with HCC with low hsa-mir-204-5p levels had better OS (Fig. 6j–l). The binding site of hsa-miR-93-5p and FAT4 in the 3'-untranslated region is shown in Fig. 6m. These findings suggested that hsa-miR-93-5p may be a FAT4-regulated miRNA in HCC.

Association Between FAT4 Expression and Immune Infiltrating Cells in HCC

Immune infiltration of HCC is closely related to patient survival and subsequent immunotherapy. In this study, we investigated whether FAT4 expression affects tumor immune infiltration. We utilized the immunedeconv package in R to analyze the score distribution of FAT4 expression in HCC. The results showed that the TIMER scores of CD4+/CD8 + T cells, neutrophils, macrophages, and dendritic cells were significantly increased in HCC tissues with high FAT4 expression (Fig. 7a). In addition, we explored the association between FAT4 expression in HCC and immune infiltration in the public TIMER database (Fig. 7b–g). Significant positive correlations were observed between FAT4 expression and the infiltration level of CD4 + T cells (r = 0.285, P = 7.39e − 08), macrophages (r = 0.17, P = 1.64e − 03), neutrophils (r = 0.274, P = 2.44e − 07), and dendritic cells (r = 0.168, P = 1.88e − 03).

Although FAT4 is associated with prognosis and several types of infiltrating immune cells in HCC, the association between FAT4 and immune markers remains unknown. We further analyzed the association between FAT4 expression and markers of immune cell subtypes in HCC using the TIMER and GEPIA2 databases (Online Resource 6). Interestingly, FAT4 expression is associated with markers of T-regulatory, monocyte, M1/M2 macrophages, T helper 1, and mast cells in HCC. In addition, FAT4 expression is associated with markers of tumor-associated macrophages, such as CCL2, CD68, and IL-10. These findings further revealed a robust interaction between FAT4 and tumor-associated macrophage infiltration. Furthermore, relationships were observed between FAT4 expression and CD4 + T cells (CD86) and dendritic cells (BDCA1 [CD1C], BDCA4 [NRP1], and CD11c [ITGAX]) (Online Resource 6).

Relationship Between Immune Checkpoints and FAT4 Expression in HCC

Given the importance of immune checkpoints in immunotherapy, we further analyzed the relationship between FAT4 expression and immune checkpoints in HCC using the TISIDB database. We found that FAT4 expression was significantly negatively correlated with several immunoinhibitors in HCC, such as LAG3, CTLA4, CD160, and PVRL2 (P < 0.05, Fig. 8a–e). Moreover, we revealed a close relationship between FAT4 expression and several immunostimulators. For example, FAT4 expression was positively correlated with the expression of IL6R, IL6, TMEM173, and CXCL12 (P < 0.05, Fig. 8f–j). These findings further showed that FAT4 expression was significantly correlated with immune infiltration in HCC.

FAT4 Expression and Prognosis in Pan-Cancer

We analyzed FAT4 expression in various cancers using the TIMER database. The results revealed that FAT4 was significantly upregulated in 13 cancer types (bladder urothelial carcinoma, breast invasive carcinoma, colon adenocarcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary carcinoma, liver HCC, lung adenocarcinoma, lung squamous cell carcinoma, prostate adenocarcinoma, rectal adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma) compared with normal samples (Fig. 9a). Next, we performed survival analysis of FAT4 in these tumors (Fig. 9b–m). Regarding OS, patients with bladder urothelial carcinoma and thyroid carcinoma with upregulated FAT4 expression exhibited good prognosis (Fig. 9a, e), and patients with kidney renal clear cell carcinoma with downregulated FAT4 expression exhibited poor prognosis (Fig. 9m). These results suggest that FAT4 plays an important role in the progression of various cancers, and can be used as a prognostic marker in several cancers.

Currently, liver biopsy is the most reliable method for diagnosing HCC. However, it is an invasive procedure with potential complications, and its clinical application is limited (Ayuso et al. 2018). Therefore, finding new non-invasive biomarkers has become important for the diagnostic and prognostic evaluation of HCC. Studies have confirmed that the serum and plasma levels of specific oncogenes in many patients with cancer are usually elevated, and that the circulating levels of oncogenes have been used for cancer classification and prognosis (He et al. 2015; Yin et al. 2015; Zhang et al. 2015). However, the genes expressed in serum are derived from various tissues, and their levels in serum vary considerably. The genes expressed in PBMCs are derived from a single source and consist of circulating immune cells, such as monocytes, T lymphocytes, B lymphocytes, and natural killer cells (Li et al. 2019b). Recently, it has been discovered that in many types of malignant tumors, gene expression in PBMCs has important clinical value for tumor diagnosis and prognosis (Foulds et al. 2018; Han et al. 2021; Raiter et al. 2020). Therefore, PBMCs are an interesting “surrogate” for evaluating the prognosis of patients with HCC. In this study, RNA-seq was used to comprehensively analyze the mRNA expression of PBMCs in patients with HCC with late survival and death. RNA-seq showed that the expression levels of DGKK, LIX1, NUAK1, CXCL10, and FAT4 in the survival group were significantly higher than those in the death group (Fig. 2c). We further verified the expression of the top 10 genes in the PBMCs of the HCC survival group. For the first time, we found that the expression level of FAT4 mRNA in PBMCs was closely related to HCC stage (Fig. 2m). These results indicate that FAT4 mRNA levels in PBMCs may be a potential biomarker and therapeutic target for the treatment of HCC.

FAT4 was first identified as a cancer suppressor in a mouse mammary epithelial cell line (Katoh 2012). Accumulating evidence suggests that FAT4 downregulation is involved in the pathogenesis of multiple malignancies, is inversely correlated with tumor grade, and exerts tumor suppressor effects (Hou et al. 2016; Jiang et al. 2018; Wei et al. 2019). Consistent with previous findings, we analyzed the pan-cancer expression of FAT4 and found that FAT4 expression was decreased in several tumors (Fig. 9a), suggesting that FAT4 may play a tumor suppressor role in various tumors. In addition, Kaplan–Meier analysis revealed that patients with kidney renal clear cell carcinoma with high FAT4 expression had a good prognosis (Fig. 9m). To elucidate the function of FAT4 in HCC, we performed GSEA and KEGG pathway analysis in the TCGA-HCC cohort and found that FAT4 was mainly involved in biological processes related to cell motility (Fig. 3a–h), and that FAT4 expression was significantly positively correlated with the PI3K/AKT/mTOR pathway, ECM degradation, transforming growth factor β, ECM-related genes, and the IL-10 anti-inflammatory signaling pathway in HCC (Fig. 4). FAT4 may play a tumor suppressor role in HCC by inhibiting tumor cell invasion and metastasis. Studies have shown that FAT4 plays an important role in inhibiting EMT and the proliferation of human gastric cancer cells (Wang et al. 2019). In colorectal cancer, FAT4 has been found to regulate PI3K activity in the PI3K/AKT/mTOR signaling pathway, inhibit tumor growth, and play an important role in preventing EMT (Wei et al. 2019). Based on these findings, we speculated that FAT4 may be abnormally expressed in HCC tissues. Using data from the GEO and TCGA databases, we found that FAT4 expression in HCC tissues was significantly lower than that in normal liver tissues (Fig. 5a–d). Moreover, we used Kaplan–Meier analysis to determine the survival relationship of FAT4 in liver cancer. The results showed that the OS of patients with HCC with high FAT4 expression was significantly prolonged (Fig. 5e). These results further suggest that FAT4 plays a tumor suppressor role in HCC, and FAT4 may serve as a novel biomarker for the prognosis of HCC.

Several studies have shown that miRNAs play an important role in the regulation of gene expression (Shi et al. 2021). To identify upstream regulatory miRNAs, we used different databases to predict potential miRNAs that may be associated with FAT4. After comprehensive analysis, miRNA-93-5p was selected as a possible miRNA for binding to FAT4 (Fig. 6). Previous studies have also shown that miR-93-5p can enhance the proliferation, migration, and invasion of gastric cancer cells by activating the Hippo pathway, which can be used as a diagnostic and therapeutic target for gastric cancer (Meng et al. 2019). In pancreatic cancer, miR-93 diminishes PTEN expression by inhibiting PI3K/Akt to promote the migration, invasion, and proliferation of pancreatic cancer (Wu et al. 2021). These results suggest that miRNA-93-5p may act as an upstream regulatory miRNA of FAT4 in HCC.

Recently, increasing evidence has shown that the tumor microenvironment and immune infiltration play key roles in the development and progression of tumors (Gajewski et al. 2013; Li et al. 2016). Immunotherapies that enhance the ability of the immune system to target and eliminate tumor cells have been shown to be effective in some patients with advanced HCC (Sperandio et al. 2022). However, there are differences in the level of immune cell infiltration and the expression levels of immune checkpoints in tumors, resulting in different clinical responses in patients (Li et al. 2016). Therefore, there is an urgent need to explore other possible HCC immunotherapy targets and precise prognostic markers of immune infiltration. Based on our findings, we further evaluated the correlation between FAT4 and immune cell infiltration in HCC using the TIMER and GEPIA2 databases. The results showed that FAT4 expression was significantly positively correlated with the infiltration of neutrophils, macrophages, dendritic cells, and CD4 + T cells (Fig. 7). At the forefront of host defense, macrophages play a key role in tumor immunity (Xia et al. 2020). CD4 + T helper cells play a vital role in humoral immunity and pathogen protection, as well as in the induction of autoimmunity (Borst et al. 2018). Dendritic cells can induce specific or non-specific immune responses, as well as play tumor-killing and antiviral roles in patients (Wculek et al. 2020). Neutrophils can infiltrate tumors. Differences in the tumor microenvironment can be the driving force or the reaction force of tumor progression (Grecian et al. 2018). These results suggest that FAT4 may play a tumor suppressor role by promoting the infiltration of tumor-associated immune cells.

In addition, immune checkpoints play an important role in the tumor immune response (Li et al. 2019a). Immune checkpoints include both stimulatory and inhibitory checkpoints. In contrast to stimulatory checkpoints, which promote the body’s immune response, inhibitory checkpoints are protective sites that prevent the body from overlying immune responses, and can reduce autoimmune responses; therefore, they are often used by tumor cells to escape the immune system (Dyck and Mills 2017; Park and Kim 2019). We used the TISIDB to analyze the relationship between FAT4 and immune checkpoint expression. FAT4 expression was positively correlated with the expression of most immune-activating factors, and negatively correlated with the expression of some immunosuppressive factors (Fig. 8). Collectively, these findings suggest that FAT4 plays an important role in recruiting and controlling immune cell infiltration in HCC.

In conclusion, we are the first to report that FAT4 expression is downregulated in the PBMCs of patients with HCC and HCC tissues, which is associated with prognosis, and is significantly correlated with diverse clinicopathological characteristics. These results indicate that the expression level of FAT4 in PBMCs and HCC tissues can be used as a prognostic marker. In addition, FAT4 expression is associated with the infiltration of antitumor-related immune cells, and the expression of immune activators. Additionally, we identified the upstream regulatory mechanism of FAT4 in HCC: the hsa-miR-93-5p/FAT4 axis. These findings provide an important understanding of the dysregulation of FAT4 in HCC, and reveal that FAT4 may play an important role in the tumor immune microenvironment. Therefore, research on FAT4 as a target has high value. Further experiments should be designed to study the specific mechanism by which FAT4 affects immune cell infiltration, to identify potential novel targets for tumor immunotherapy. However, the present study has some limitations. The number of cases was small; therefore, more clinical data should be used for verification. However, experiments have not been conducted to verify the function of hsa-miR-93-5P/FAT4 in HCC development. Our future work will focus on validating the role of the hsa-miR-93-5p/FAT4 axis in regulating immune cell infiltration in HCC by analyzing experimental techniques, such as immunohistochemistry and flow cytometry.

Funding: This work was supported by the Shenzhen Science and Technology Project (Grant numbers JCYJ20170817094901026 and JCYJ20180302173542393).

Conflict of Interest: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: Xiaozhou Zhou contributed to the study conception and design. Jing Li, Qi Huang, MengQing Ma, and Minling Lv acquired, analyzed, and interpreted the data. Jing Li, Minling Lv, and Qi Huang drafted and revised the manuscript. Rui Hu and Xin Zhong performed data analysis. Wei Zhang, Renjie Zhang, MengQing Ma, and Xinfeng Sun assisted with specimen collection. Zhiyi Han, Wenfeng Ma, and Wenxing Feng analyzed the data and revised the manuscript. All authors read and approved the final manuscript.

Data Availability: All data generated or analyzed during this study are included in this published article and its Supplementary Information files. Further inquiries can be directed to the corresponding author.

Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shenzhen Hospital of Traditional Chinese Medicine (2018/58).

Consent to Participate: Informed consent was obtained from all individual participants included in the study.

Consent to Publish: The authors affirm that human research participants provided informed consent for publication of potentially identifiable images or data included in this article.

Ayuso C, Rimola J, Vilana R, Burrel M, Darnell A, García-Criado Á, Bianchi L, Belmonte E, Caparroz C, Barrufet M, Bruix J, Brú C (2018) Diagnosis and staging of hepatocellular carcinoma (HCC): current guidelines. Eur J Radiol 101:72–81. https://doi.org/10.1016/j.ejrad.2018.01.025
Borst J, Ahrends T, Bąbała N, Melief CJM, Kastenmüller W (2018) CD4 + T cell help in cancer immunology and immunotherapy. Nat Rev Immunol 18:635–647. https://doi.org/10.1038/s41577-018-0044-0
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, Varambally S (2017) UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 19:649–658. https://doi.org/10.1016/j.neo.2017.05.002
Chang DH, Rutledge JR, Patel AA, Heerdt BG, Augenlicht LH, Korst RJ (2013) The effect of lung cancer on cytokine expression in peripheral blood mononuclear cells. PLOS ONE 8:e64456. https://doi.org/10.1371/journal.pone.0064456
Dyck L, Mills KHG (2017) Immune checkpoints and their inhibition in cancer and infectious diseases. Eur J Immunol 47:765–779. https://doi.org/10.1002/eji.201646875
Foulds GA, Vadakekolathu J, Abdel-Fatah TMA, Nagarajan D, Reeder S, Johnson C, Hood S, Moseley PM, Chan SYT, Pockley AG, Rutella S, McArdle SEB (2018) Immune-phenotyping and transcriptomic profiling of peripheral blood mononuclear cells from patients with breast cancer: identification of a 3 gene signature which predicts relapse of triple negative breast cancer. Front Immunol 9:2028. https://doi.org/10.3389/fimmu.2018.02028
Gajewski TF, Schreiber H, Fu YX (2013) Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 14:1014–1022. https://doi.org/10.1038/ni.2703
Grecian R, Whyte MKB, Walmsley SR (2018) The role of neutrophils in cancer. Br Med Bull 128:5–14. https://doi.org/10.1093/bmb/ldy029
Han Z, Feng W, Hu R, Ge Q, Ma W, Zhang W, Xu S, Zhan B, Zhang L, Sun X, Zhou X (2021) RNA-seq profiling reveals PBMC RNA as a potential biomarker for hepatocellular carcinoma. Sci Rep 11:17797. https://doi.org/10.1038/s41598-021-96952-x
He S, Zhang DC, Wei C (2015) MicroRNAs as biomarkers for hepatocellular carcinoma diagnosis and prognosis. Clin Res Hepatol Gastroenterol 39:426–434. https://doi.org/10.1016/j.clinre.2015.01.006
Hogan SA, Courtier A, Cheng PF, Jaberg-Bentele NF, Goldinger SM, Manuel M, Perez S, Plantier N, Mouret JF, Nguyen-Kim TDL, Raaijmakers MIG, Kvistborg P, Pasqual N, Haanen JBAG, Dummer R, Levesque MP (2019) Peripheral blood TCR repertoire profiling may facilitate patient stratification for immunotherapy against melanoma. Cancer Immunol Res 7:77–85. https://doi.org/10.1158/2326-6066.CIR-18-0136
Hou L, Chen M, Zhao X, Li J, Deng S, Hu J, Yang H, Jiang J (2016) FAT4 functions as a tumor suppressor in triple-negative breast cancer. Tumour Biol 37:16337–16343. https://doi.org/10.1007/s13277-016-5421-3
Jiang X, Liu Z, Xia Y, Luo J, Xu J, He X, Tao H (2018) Low FAT4 expression is associated with a poor prognosis in gastric cancer patients. Oncotarget 9:5137–5154. https://doi.org/10.18632/oncotarget.23702
Jiang Y, Han QJ, Zhang J (2019) Hepatocellular carcinoma: mechanisms of progression and immunotherapy. World J Gastroenterol 25:3151–3167. https://doi.org/10.3748/wjg.v25.i25.3151
Katoh M (2012) Function and cancer genomics of FAT family genes (review). Int J Oncol 41:1913–1918. https://doi.org/10.3892/ijo.2012.1669
Lánczky A, Győrffy B (2021) Web-based survival analysis tool tailored for medical research (KmPlot): development and implementation. J Med Internet Res 23:e27633. https://doi.org/10.2196/27633
Li B, Chan HL, Chen P (2019) Immune checkpoint inhibitors: basics and challenges. Curr Med Chem 26:3009–3025. https://doi.org/10.2174/0929867324666170804143706
Li B, Severson E, Pignon JC, Zhao H, Li T, Novak J, Jiang P, Shen H, Aster JC, Rodig S, Signoretti S, Liu JS, Liu XS (2016) Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol 17:174. https://doi.org/10.1186/s13059-016-1028-7
Li JH, Liu S, Zhou H, Qu LH, Yang JH (2014) starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res v2.0 42:D92-D97. https://doi.org/10.1093/nar/gkt1248
Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS (2020) TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 48:W509-W514. https://doi.org/10.1093/nar/gkaa407
Li Z, Yang X, Zhang Y, Yang X, Cui X, Zhang Y, Gong W, Bai H, Liu N, Tang Z, Guo M, Li K, Zhang T, Wang L, Song X (2019) A human peripheral blood mononuclear cell (PBMC) engrafted humanized xenograft model for translational immuno-oncology (I-O) research. J Vis Exp 15:e59679. https://doi.org/10.3791/59679
Llovet JM, Brú C, Bruix J (1999) Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis 19:329–338. https://doi.org/10.1055/s-2007-1007122
Meng H, Li YY, Han D, Zhang CY (2019) MiRNA-93-5p promotes the biological progression of gastric cancer cells via Hippo signaling pathway. Eur Rev Med Pharmacol Sci 23:4763–4769.
Pallett LJ, Burton AR, Amin OE, Rodriguez-Tajes S, Patel AA, Zakeri N, Jeffery-Smith A, Swadling L, Schmidt NM, Baiges A, Gander A, Yu D, Nasralla D, Froghi F, Iype S, Davidson BR, Thorburn D, Yona S, Forns X, Maini MK (2020) Longevity and replenishment of human liver-resident memory T cells and mononuclear phagocytes. J Exp Med 217:e20200050. https://doi.org/10.1084/jem.20200050
Parikh ND, Pillai A (2021) Recent advances in hepatocellular carcinoma treatment. Clin Gastroenterol Hepatol 19:2020–2024. https://doi.org/10.1016/j.cgh.2021.05.045
Park SY, Kim IS (2019) Harnessing immune checkpoints in myeloid lineage cells for cancer immunotherapy. Cancer Lett 452:51–58. https://doi.org/10.1016/j.canlet.2019.03.018
Qureshi A, Michel M, Lerner J, Dasanu CA (2021) Evolving therapeutic strategies for advanced hepatocellular carcinoma. Expert Opin Pharmacother 22:2495–2506. https://doi.org/10.1080/14656566.2021.1953473
Raiter A, Lipovetzki J, Lubin I, Yerushalmi R (2020) GRP78 expression in peripheral blood mononuclear cells is a new predictive marker for the benefit of taxanes in breast cancer neoadjuvant treatment. BMC Cancer 20:333. https://doi.org/10.1186/s12885-020-06835-z
Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, Chan NW, Zhang J. (2019) TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics 35:4200–4202. https://doi.org/10.1093/bioinformatics/btz210
Salman H, Ori Y, Bergman M, Djaldetti M, Bessler H (2012) Human prostate cancer cells induce inflammatory cytokine secretion by peripheral blood mononuclear cells. Biomed Pharmacother 66:330–333. https://doi.org/10.1016/j.biopha.2012.03.004
Shi Y, Liu Z, Lin Q, Luo Q, Cen Y, Li J, Fang X, Gong C (2021) MiRNAs and cancer: key link in diagnosis and therapy. Genes (Basel) 12:1289. https://doi.org/10.3390/genes12081289
Sperandio RC, Pestana RC, Miyamura BV, Kaseb AO (2022) Hepatocellular carcinoma immunotherapy. Annu Rev Med 73:267–278. https://doi.org/10.1146/annurev-med-042220-021121
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45:W98-W102. https://doi.org/10.1093/nar/gkx247
Vasaikar SV, Straub P, Wang J, Zhang B (2018) LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 46:D956-D963. https://doi.org/10.1093/nar/gkx1090
Wallace MC, Preen D, Jeffrey GP, Adams LA (2015) The evolving epidemiology of hepatocellular carcinoma: a global perspective. Expert Rev Gastroenterol Hepatol 9:765–779. https://doi.org/10.1586/17474124.2015.1028363
Wang CY, Li S (2019) Clinical characteristics and prognosis of 2887 patients with hepatocellular carcinoma: a single center 14 years experience from China. Med (Baltimore) 98:e14070. https://doi.org/10.1097/MD.0000000000014070
Wang L, Li K, Wang C, Shi X, Yang H (2019) miR-107 regulates growth and metastasis of gastric cancer cells via activation of the PI3K-AKT signaling pathway by down-regulating FAT4. Cancer Med 8:5264–5273. https://doi.org/10.1002/cam4.2396
Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D (2020) Dendritic cells in cancer immunology and immunotherapy. Nat Rev Immunol 20:7–24. https://doi.org/10.1038/s41577-019-0210-z
Wei R, Xiao Y, Song Y, Yuan H, Luo J, Xu W (2019) FAT4 regulates the EMT and autophagy in colorectal cancer cells in part via the PI3K-AKT signaling axis. J Exp Clin Cancer Res 38:112. https://doi.org/10.1186/s13046-019-1043-0
Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, Chiang EY, Iftikhar H, O’Gorman WE, Au-Yeung A, Takahashi C, Goldstein LD, Poon C, Keerthivasan S, de Almeida Nagata DE, Du X, Lee HM, Banta KL, Mariathasan S, Das Thakur M, Huseni MA, Ballinger M, Estay I, Caplazi P, Modrusan Z, Delamarre L, Mellman I, Bourgon R, Grogan JL (2020) Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579:274–278. https://doi.org/10.1038/s41586-020-2056-8
Wu Y, Xu W, Yang Y, Zhang Z (2021) miRNA-93-5p promotes gemcitabine resistance in pancreatic cancer cells by targeting the PTEN-mediated PI3K/Akt signaling pathway. Ann Clin Lab Sci 51:310–320.
Xia Y, Rao L, Yao H, Wang Z, Ning P, Chen X (2020) Engineering macrophages for cancer immunotherapy and drug delivery. Adv Mater 32:e2002054. https://doi.org/10.1002/adma.202002054
Yan S, Wan G (2021) Tumor-associated macrophages in immunotherapy. FEBS Journal 288:6174–6186. https://doi.org/10.1111/febs.15726
Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR (2019) A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol 16:589–604. https://doi.org/10.1038/s41575-019-0186-y
Yin W, Zhao Y, Ji YJ, Tong LP, Liu Y, He SX, Wang AQ (2015) Serum/plasma microRNAs as biomarkers for HBV-related hepatocellular carcinoma in China. BioMed Res Int 2015:965185. https://doi.org/10.1155/2015/965185
Zhang HH, Chen HS (2020) [Strategies and challenges of immunotherapy for hepatocellular carcinoma]. Zhonghua Gan Zang Bing Za Zhi 28:457–460. https://doi.org/10.3760/cma.j.cn501113-20200602-00288
Zhang YC, Xu Z, Zhang TF, Wang YL (2015) Circulating microRNAs as diagnostic and prognostic tools for hepatocellular carcinoma. World J Gastroenterol 21:9853–9862. https://doi.org/10.3748/wjg.v21.i34.9853

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

FAT4 Overexpression in Peripheral Blood Mononuclear Cells and Tumor Tissues is Associated With a Favorable Prognosis and Promotes Immune Cell Infiltration in Hepatocellular Carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Patient Samples

RNA-Seq Library

Differentially Expressed Genes (DEGs)

Gene Expression Analysis

Survival Analysis

Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction Network

Analysis of FAT4 Signaling Pathways in HCC

Prediction of Upstream miRNAs Regulating FAT4

Immune Infiltration Analysis

Statistical Analysis

Results

Identification of Prognostically Relevant Genes in PBMCs of Patients With HCC

Analysis of FAT4 Function in HCC

Analysis of FAT4-Related Signaling Pathways in HCC

FAT4 Expression in HCC and Prognosis

FAT4 Downregulation is Correlated With MiR-93-5p Upregulation in HCC

Association Between FAT4 Expression and Immune Infiltrating Cells in HCC

Relationship Between Immune Checkpoints and FAT4 Expression in HCC

FAT4 Expression and Prognosis in Pan-Cancer

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1